A K-means Inspired Solution Framework for Large-Scale Multi-Traveling Salesman Problems

TL;DR

This paper introduces a K-means-inspired framework for large-scale multi-agent task allocation, significantly reducing computational costs while maintaining high solution quality in scenarios with thousands of agents and targets.

Contribution

It presents a novel clustering-based approach that reformulates the MTSP as a classification problem, enabling efficient large-scale task allocation.

Findings

Maintains high solution quality in large-scale scenarios

Reduces computational complexity significantly

Effective in scenarios with 1000 agents and 5000 targets

Abstract

The Multi-Traveling Salesman Problem (MTSP) is a commonly used mathematical model for multi-agent task allocation. However, as the number of agents and task targets increases, existing optimization-based methods often incur prohibitive computational costs, posing significant challenges to large-scale coordination in unmanned systems. To address this issue, this paper proposes a K-means-inspired task allocation framework that reformulates the MTSP as a spatially constrained classification process. By leveraging spatial coherence, the proposed method enables fast estimation of path costs and efficient task grouping, thereby fundamentally reducing overall computational complexity. Extensive simulation results demonstrate that the framework can maintain high solution quality even in extremely large-scale scenarios-for instance, in tasks involving 1000 agents and 5000 targets. The findings indicate that this "cluster-then-route" decomposition strategy offers an efficient and reliable solution for large-scale multi-agent task allocation.